5,176 research outputs found
Noise-Resilient Group Testing: Limitations and Constructions
We study combinatorial group testing schemes for learning -sparse Boolean
vectors using highly unreliable disjunctive measurements. We consider an
adversarial noise model that only limits the number of false observations, and
show that any noise-resilient scheme in this model can only approximately
reconstruct the sparse vector. On the positive side, we take this barrier to
our advantage and show that approximate reconstruction (within a satisfactory
degree of approximation) allows us to break the information theoretic lower
bound of that is known for exact reconstruction of
-sparse vectors of length via non-adaptive measurements, by a
multiplicative factor .
Specifically, we give simple randomized constructions of non-adaptive
measurement schemes, with measurements, that allow efficient
reconstruction of -sparse vectors up to false positives even in the
presence of false positives and false negatives within the
measurement outcomes, for any constant . We show that, information
theoretically, none of these parameters can be substantially improved without
dramatically affecting the others. Furthermore, we obtain several explicit
constructions, in particular one matching the randomized trade-off but using measurements. We also obtain explicit constructions
that allow fast reconstruction in time \poly(m), which would be sublinear in
for sufficiently sparse vectors. The main tool used in our construction is
the list-decoding view of randomness condensers and extractors.Comment: Full version. A preliminary summary of this work appears (under the
same title) in proceedings of the 17th International Symposium on
Fundamentals of Computation Theory (FCT 2009
Self-replication and evolution of DNA crystals
Is it possible to create a simple physical system that is capable of replicating itself? Can such a system evolve interesting behaviors, thus allowing it to adapt to a wide range of environments? This paper presents a design for such a replicator constructed exclusively from synthetic DNA. The basis for the replicator is crystal growth: information is stored in the spatial arrangement of monomers and copied from layer to layer by templating. Replication is achieved by fragmentation of crystals, which produces new crystals that carry the same information. Crystal replication avoids intrinsic problems associated with template-directed mechanisms for replication of one-dimensional polymers. A key innovation of our work is that by using programmable DNA tiles as the crystal monomers, we can design crystal growth processes that apply interesting selective pressures to the evolving sequences. While evolution requires that copying occur with high accuracy, we show how to adapt error-correction techniques from algorithmic self-assembly to lower the replication error rate as much as is required
Two computational primitives for algorithmic self-assembly: Copying and counting
Copying and counting are useful primitive operations for computation and construction. We have made DNA crystals that copy and crystals that count as they grow. For counting, 16 oligonucleotides assemble into four DNA Wang tiles that subsequently crystallize on a polymeric nucleating scaffold strand, arranging themselves in a binary counting pattern that could serve as a template for a molecular electronic demultiplexing circuit. Although the yield of counting crystals is low, and per-tile error rates in such crystals is roughly 10%, this work demonstrates the potential of algorithmic self-assembly to create complex nanoscale patterns of technological interest. A subset of the tiles for counting form information-bearing DNA tubes that copy bit strings from layer to layer along their length
Error Correction in DNA Computing: Misclassification and Strand Loss
We present a method of transforming an extract-based DNA computation that is error-prone into one that is relatively error-free. These improvements in error rates are achieved without the supposition of any improvements in the reliability of the underlying laboratory techniques. We assume that only two types of errors are possible: a DNA strand may be incorrectly processed or it may be lost entirely. We show to deal with each of these
errors individually and then analyze the tradeoff when both must be optimized simultaneously
Toward reliable algorithmic self-assembly of DNA tiles: A fixed-width cellular automaton pattern
Bottom-up fabrication of nanoscale structures relies on chemical processes to direct self-assembly. The complexity, precision, and yield achievable by a one-pot reaction are limited by our ability to encode assembly instructions into the molecules themselves. Nucleic acids provide a platform for investigating these issues, as molecular structure and intramolecular interactions can encode growth rules. Here, we use DNA tiles and DNA origami to grow crystals containing a cellular automaton pattern. In a one-pot annealing reaction, 250 DNA strands first assemble into a set of 10 free tile types and a seed structure, then the free tiles grow algorithmically from the seed according to the automaton rules. In our experiments, crystals grew to ~300 nm long, containing ~300 tiles with an initial assembly error rate of ~1.4% per tile. This work provides evidence that programmable molecular self-assembly may be sufficient to create a wide range of complex objects in one-pot reactions
Proofreading tile sets: Error correction for algorithmic self-assembly
For robust molecular implementation of tile-based algorithmic
self-assembly, methods for reducing errors must be developed. Previous
studies suggested that by control of physical conditions, such as
temperature and the concentration of tiles, errors (ε) can be reduced
to an arbitrarily low rate - but at the cost of reduced speed (r) for
the self-assembly process. For tile sets directly implementing blocked
cellular automata, it was shown that r ≈ βε^2 was optimal. Here, we
show that an improved construction, which we refer to as proofreading
tile sets, can in principle exploit the cooperativity of tile assembly reactions
to dramatically improve the scaling behavior to r ≈ βε and better.
This suggests that existing DNA-based molecular tile approaches may be
improved to produce macroscopic algorithmic crystals with few errors.
Generalizations and limitations of the proofreading tile set construction
are discussed
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